


import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
			    MODEL_MAPPING,
			    LevitForImageClassification,
			    LevitForImageClassificationWithTeacher,
			    LevitModel,
			)
			from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import LevitImageProcessor

class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							def        __snake_case   (				self			:    Tuple							)      ->       List[str]:
														__snake_case						:     List[Any]									=  self.config_class(**self.inputs_dict							)
														self.parent.assertTrue(hasattr(lowerCamelCase       ,      "hidden_sizes"							)							)
														self.parent.assertTrue(hasattr(lowerCamelCase       ,      "num_attention_heads"							)							)

class 							a						:




							"""simple docstring"""


							def __init__(				self			:    Any       ,      lowerCamelCase			:    Dict       ,      lowerCamelCase			:    List[str]=13       ,      lowerCamelCase			:    Union[str, Any]=64       ,      lowerCamelCase			:    Any=3       ,      lowerCamelCase			:    int=3       ,      lowerCamelCase			:    Union[str, Any]=2       ,      lowerCamelCase			:    Tuple=1       ,      lowerCamelCase			:    Optional[Any]=16       ,      lowerCamelCase			:    Dict=[128, 256, 384]       ,      lowerCamelCase			:    Union[str, Any]=[4, 6, 8]       ,      lowerCamelCase			:    Any=[2, 3, 4]       ,      lowerCamelCase			:    int=[16, 16, 16]       ,      lowerCamelCase			:    List[str]=0       ,      lowerCamelCase			:    List[Any]=[2, 2, 2]       ,      lowerCamelCase			:    List[str]=[2, 2, 2]       ,      lowerCamelCase			:    Tuple=0.02       ,      lowerCamelCase			:    int=True       ,      lowerCamelCase			:    Any=True       ,      lowerCamelCase			:    Any=2       ,      )      ->       Optional[int]:
														__snake_case						:     Any									=  parent
														__snake_case						:     List[Any]									=  batch_size
														__snake_case						:     Any									=  image_size
														__snake_case						:     Optional[int]									=  num_channels
														__snake_case						:     Union[str, Any]									=  kernel_size
														__snake_case						:     Dict									=  stride
														__snake_case						:     Any									=  padding
														__snake_case						:     Tuple									=  hidden_sizes
														__snake_case						:     Optional[Any]									=  num_attention_heads
														__snake_case						:     int									=  depths
														__snake_case						:     int									=  key_dim
														__snake_case						:     Any									=  drop_path_rate
														__snake_case						:     List[Any]									=  patch_size
														__snake_case						:     str									=  attention_ratio
														__snake_case						:     Optional[Any]									=  mlp_ratio
														__snake_case						:     str									=  initializer_range
														__snake_case						:     Dict									=  [
														    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
														    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
														]
														__snake_case						:     str									=  is_training
														__snake_case						:     Any									=  use_labels
														__snake_case						:     List[str]									=  num_labels
														__snake_case						:     Any									=  initializer_range

							def        __snake_case   (				self			:    Tuple							)      ->       Optional[int]:
														__snake_case						:     Optional[int]									=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

														__snake_case						:     Any									=  None
														if self.use_labels:
																					__snake_case						:     Optional[Any]									=  ids_tensor([self.batch_size]       ,      self.num_labels							)

														__snake_case						:     int									=  self.get_config()
														return config, pixel_values, labels

							def        __snake_case   (				self			:    Dict							)      ->       Optional[int]:
														return LevitConfig(
														    image_size=self.image_size       ,      num_channels=self.num_channels       ,      kernel_size=self.kernel_size       ,      stride=self.stride       ,      padding=self.padding       ,      patch_size=self.patch_size       ,      hidden_sizes=self.hidden_sizes       ,      num_attention_heads=self.num_attention_heads       ,      depths=self.depths       ,      key_dim=self.key_dim       ,      drop_path_rate=self.drop_path_rate       ,      mlp_ratio=self.mlp_ratio       ,      attention_ratio=self.attention_ratio       ,      initializer_range=self.initializer_range       ,      down_ops=self.down_ops       ,      )

							def        __snake_case   (				self			:    str       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    Optional[Any]       ,      lowerCamelCase			:    str							)      ->       str:
														__snake_case						:     List[Any]									=  LevitModel(config=lowerCamelCase							)
														model.to(lowerCamelCase							)
														model.eval()
														__snake_case						:     Optional[Any]									=  model(lowerCamelCase							)
														__snake_case						:     Dict									=  (self.image_size, self.image_size)
														__snake_case					,     __snake_case						:     List[str]									=  image_size[0], image_size[1]
														for _ in range(4							):
																					__snake_case						:     int									=  floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
																					__snake_case						:     Optional[int]									=  floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1							)
														self.parent.assertEqual(
														    result.last_hidden_state.shape       ,      (self.batch_size, ceil(height / 4							) * ceil(width / 4							), self.hidden_sizes[-1])       ,      )

							def        __snake_case   (				self			:    List[str]       ,      lowerCamelCase			:    str       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    int							)      ->       Optional[int]:
														__snake_case						:     int									=  self.num_labels
														__snake_case						:     int									=  LevitForImageClassification(lowerCamelCase							)
														model.to(lowerCamelCase							)
														model.eval()
														__snake_case						:     str									=  model(lowerCamelCase       ,      labels=lowerCamelCase							)
														self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.num_labels)							)






							def        __snake_case   (				self			:    Any							)      ->       Dict:
														__snake_case						:     Any									=  self.prepare_config_and_inputs()
														__snake_case					,     __snake_case					,     __snake_case						:     str									=  config_and_inputs
														__snake_case						:     Any									=  {"pixel_values": pixel_values}
														return config, inputs_dict

@require_torch
class 							a						(_lowerCAmelCase	,							_lowerCAmelCase	,							unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				str	    =    (
							    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
							    if is_torch_available()
							    else ()
							)
							__UpperCAmelCase       :				Optional[int]	    =    (
							    {
							        "feature-extraction": LevitModel,
							        "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
							    }
							    if is_torch_available()
							    else {}
							)

							__UpperCAmelCase       :				Dict	    =    False
							__UpperCAmelCase       :				Optional[Any]	    =    False
							__UpperCAmelCase       :				int	    =    False
							__UpperCAmelCase       :				Union[str, Any]	    =    False
							__UpperCAmelCase       :				Tuple	    =    False

							def        __snake_case   (				self			:    Optional[Any]							)      ->       Any:
														__snake_case						:     int									=  LevitModelTester(self							)
														__snake_case						:     Optional[int]									=  ConfigTester(self       ,      config_class=lowerCamelCase       ,      has_text_modality=lowerCamelCase       ,      hidden_size=37							)

							def        __snake_case   (				self			:    Optional[int]							)      ->       Tuple:
														self.create_and_test_config_common_properties()
														self.config_tester.create_and_test_config_to_json_string()
														self.config_tester.create_and_test_config_to_json_file()
														self.config_tester.create_and_test_config_from_and_save_pretrained()
														self.config_tester.create_and_test_config_with_num_labels()
														self.config_tester.check_config_can_be_init_without_params()
														self.config_tester.check_config_arguments_init()

							def        __snake_case   (				self			:    Dict							)      ->       str:
														return

							@unittest.skip(reason="Levit does not use inputs_embeds"							)
							def        __snake_case   (				self			:    Any							)      ->       Optional[int]:
														pass

							@unittest.skip(reason="Levit does not support input and output embeddings"							)
							def        __snake_case   (				self			:    Dict							)      ->       int:
														pass

							@unittest.skip(reason="Levit does not output attentions"							)
							def        __snake_case   (				self			:    Optional[int]							)      ->       Optional[int]:
														pass

							def        __snake_case   (				self			:    Tuple							)      ->       Tuple:
														__snake_case					,     __snake_case						:     Optional[int]									=  self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					__snake_case						:     Optional[Any]									=  model_class(lowerCamelCase							)
																					__snake_case						:     Dict									=  inspect.signature(model.forward							)
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					__snake_case						:     Tuple									=  [*signature.parameters.keys()]

																					__snake_case						:     Any									=  ["pixel_values"]
																					self.assertListEqual(arg_names[:1]       ,      lowerCamelCase							)

							def        __snake_case   (				self			:    int							)      ->       Tuple:
														def check_hidden_states_output(lowerCamelCase			:    str       ,      lowerCamelCase			:    Tuple       ,      lowerCamelCase			:    Any							):
																					__snake_case						:     List[str]									=  model_class(lowerCamelCase							)
																					model.to(lowerCamelCase							)
																					model.eval()

																					with torch.no_grad():
																												__snake_case						:     Optional[Any]									=  model(**self._prepare_for_class(lowerCamelCase       ,      lowerCamelCase							)							)

																					__snake_case						:     str									=  outputs.hidden_states

																					__snake_case						:     Tuple									=  len(self.model_tester.depths							) + 1
																					self.assertEqual(len(lowerCamelCase							)       ,      lowerCamelCase							)

																					__snake_case						:     Optional[int]									=  (self.model_tester.image_size, self.model_tester.image_size)
																					__snake_case					,     __snake_case						:     List[str]									=  image_size[0], image_size[1]
																					for _ in range(4							):
																												__snake_case						:     Tuple									=  floor(
																												    (
																												        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																												        / self.model_tester.stride
																												    )
																												    + 1							)
																												__snake_case						:     Dict									=  floor(
																												    (
																												        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
																												        / self.model_tester.stride
																												    )
																												    + 1							)
																					# verify the first hidden states (first block)
																					self.assertListEqual(
																					    list(hidden_states[0].shape[-2:]							)       ,      [
																					        height * width,
																					        self.model_tester.hidden_sizes[0],
																					    ]       ,      )

														__snake_case					,     __snake_case						:     Union[str, Any]									=  self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					__snake_case						:     str									=  True
																					check_hidden_states_output(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					__snake_case						:     Tuple									=  True

																					check_hidden_states_output(lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)

							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."							)
							def        __snake_case   (				self			:    Any							)      ->       Union[str, Any]:
														pass

							def        __snake_case   (				self			:    Tuple       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    str       ,      lowerCamelCase			:    Union[str, Any]=False							)      ->       List[str]:
														__snake_case						:     Union[str, Any]									=  super()._prepare_for_class(lowerCamelCase       ,      lowerCamelCase       ,      return_labels=lowerCamelCase							)

														if return_labels:
																					if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																												del inputs_dict["labels"]

														return inputs_dict

							def        __snake_case   (				self			:    str							)      ->       Union[str, Any]:
														__snake_case						:     Any									=  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*lowerCamelCase							)

							def        __snake_case   (				self			:    List[Any]							)      ->       Union[str, Any]:
														__snake_case						:     str									=  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*lowerCamelCase							)

							def        __snake_case   (				self			:    Union[str, Any]							)      ->       Any:
														if not self.model_tester.is_training:
																					return

														__snake_case					,     __snake_case						:     List[str]									=  self.model_tester.prepare_config_and_inputs_for_common()
														__snake_case						:     str									=  True

														for model_class in self.all_model_classes:
																					# LevitForImageClassificationWithTeacher supports inference-only
																					if (
																					    model_class in get_values(lowerCamelCase							)
																					    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																					):
																												continue
																					__snake_case						:     List[str]									=  model_class(lowerCamelCase							)
																					model.to(lowerCamelCase							)
																					model.train()
																					__snake_case						:     str									=  self._prepare_for_class(lowerCamelCase       ,      lowerCamelCase       ,      return_labels=lowerCamelCase							)
																					__snake_case						:     Optional[Any]									=  model(**lowerCamelCase							).loss
																					loss.backward()

							def        __snake_case   (				self			:    List[Any]							)      ->       Any:
														__snake_case					,     __snake_case						:     Any									=  self.model_tester.prepare_config_and_inputs_for_common()
														if not self.model_tester.is_training:
																					return

														__snake_case						:     List[str]									=  False
														__snake_case						:     List[str]									=  True

														for model_class in self.all_model_classes:
																					if model_class in get_values(lowerCamelCase							) or not model_class.supports_gradient_checkpointing:
																												continue
																					# LevitForImageClassificationWithTeacher supports inference-only
																					if model_class.__name__ == "LevitForImageClassificationWithTeacher":
																												continue
																					__snake_case						:     int									=  model_class(lowerCamelCase							)
																					model.gradient_checkpointing_enable()
																					model.to(lowerCamelCase							)
																					model.train()
																					__snake_case						:     List[Any]									=  self._prepare_for_class(lowerCamelCase       ,      lowerCamelCase       ,      return_labels=lowerCamelCase							)
																					__snake_case						:     Tuple									=  model(**lowerCamelCase							).loss
																					loss.backward()

							def        __snake_case   (				self			:    List[Any]							)      ->       Dict:
														__snake_case					,     __snake_case						:     Any									=  self.model_tester.prepare_config_and_inputs_for_common()

														__snake_case						:     Any									=  [
														    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
														    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
														    {"title": "regression", "num_labels": 1, "dtype": torch.float},
														]

														for model_class in self.all_model_classes:
																					if (
																					    model_class
																					    not in [
																					        *get_values(lowerCamelCase							),
																					    ]
																					    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
																					):
																												continue

																					for problem_type in problem_types:
																												with self.subTest(msg=F'Testing {model_class} with {problem_type["title"]}'							):
																																			__snake_case						:     List[str]									=  problem_type["title"]
																																			__snake_case						:     Any									=  problem_type["num_labels"]

																																			__snake_case						:     Dict									=  model_class(lowerCamelCase							)
																																			model.to(lowerCamelCase							)
																																			model.train()

																																			__snake_case						:     str									=  self._prepare_for_class(lowerCamelCase       ,      lowerCamelCase       ,      return_labels=lowerCamelCase							)

																																			if problem_type["num_labels"] > 1:
																																										__snake_case						:     str									=  inputs["labels"].unsqueeze(1							).repeat(1       ,      problem_type["num_labels"]							)

																																			__snake_case						:     Dict									=  inputs["labels"].to(problem_type["dtype"]							)

																																			# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																																			# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																																			# they have the same size." which is a symptom something in wrong for the regression problem.
																																			# See https://github.com/huggingface/transformers/issues/11780
																																			with warnings.catch_warnings(record=lowerCamelCase							) as warning_list:
																																										__snake_case						:     Optional[Any]									=  model(**lowerCamelCase							).loss
																																			for w in warning_list:
																																										if "Using a target size that is different to the input size" in str(w.message							):
																																																	raise ValueError(
																																																	    F'Something is going wrong in the regression problem: intercepted {w.message}'							)

																																			loss.backward()






							@slow
							def        __snake_case   (				self			:    Tuple							)      ->       Optional[int]:
														for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					__snake_case						:     Any									=  LevitModel.from_pretrained(lowerCamelCase							)
																					self.assertIsNotNone(lowerCamelCase							)







def 							lowerCAmelCase_	(   ):
							__snake_case						:     List[Any]									=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
							return image

@require_torch
@require_vision
class 							a						(unittest.TestCase ):




							"""simple docstring"""


							@cached_property
							def        __snake_case   (				self			:    Tuple							)      ->       str:
														return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)






							@slow
							def        __snake_case   (				self			:    List[Any]							)      ->       Any:
														__snake_case						:     Optional[Any]									=  LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(
														    lowerCamelCase							)

														__snake_case						:     Dict									=  self.default_image_processor
														__snake_case						:     Tuple									=  prepare_img()
														__snake_case						:     List[Any]									=  image_processor(images=lowerCamelCase       ,      return_tensors="pt"							).to(lowerCamelCase							)

														# forward pass
														with torch.no_grad():
																					__snake_case						:     int									=  model(**lowerCamelCase							)

														# verify the logits
														__snake_case						:     Tuple									=  torch.Size((1, 1000)							)
														self.assertEqual(outputs.logits.shape       ,      lowerCamelCase							)

														__snake_case						:     Dict									=  torch.tensor([1.04_48, -0.37_45, -1.83_17]							).to(lowerCamelCase							)

														self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,      lowerCamelCase       ,      atol=1E-4							)							)



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Tuple          =			(CMStochasticIterativeScheduler,)
			__UpperCamelCase   :    List[str]          =			10

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						**snake_case_			:							List[str]   ):
							UpperCamelCase_:				Optional[int]						=    {
							    """num_train_timesteps""": 201,
							    """sigma_min""": 0.002,
							    """sigma_max""": 80.0,
							}

							config.update(**snake_case_   )
							return config

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_:				Tuple						=    10

							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    self.scheduler_classes[0](**snake_case_   )

							scheduler.set_timesteps(snake_case_   )

							UpperCamelCase_:				int						=    scheduler.timesteps[0]
							UpperCamelCase_:				str						=    scheduler.timesteps[1]

							UpperCamelCase_:				Optional[int]						=    self.dummy_sample
							UpperCamelCase_:				int						=    0.1 * sample

							UpperCamelCase_:				Optional[Any]						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample
							UpperCamelCase_:				Any						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_   ).prev_sample

							self.assertEqual(output_a.shape    ,						sample.shape   )
							self.assertEqual(output_a.shape    ,						output_a.shape   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[Any]   ):
							for clip_denoised in [True, False]:
											self.check_over_configs(clip_denoised=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Any   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				List[str]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Optional[int]						=    1
							scheduler.set_timesteps(snake_case_   )
							UpperCamelCase_:				Any						=    scheduler.timesteps

							UpperCamelCase_:				int						=    torch.manual_seed(0   )

							UpperCamelCase_:				Any						=    self.dummy_model()
							UpperCamelCase_:				Union[str, Any]						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for i, t in enumerate(snake_case_   ):
											# 1. scale model input
											UpperCamelCase_:				str						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				List[Any]						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				Union[str, Any]						=    pred_prev_sample

							UpperCamelCase_:				int						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				List[Any]						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 192.7614   ) < 1e-2
							assert abs(result_mean.item() - 0.2510   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Dict						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[Any]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				str						=    [106, 0]
							scheduler.set_timesteps(timesteps=snake_case_   )
							UpperCamelCase_:				int						=    scheduler.timesteps

							UpperCamelCase_:				List[Any]						=    torch.manual_seed(0   )

							UpperCamelCase_:				List[Any]						=    self.dummy_model()
							UpperCamelCase_:				int						=    self.dummy_sample_deter * scheduler.init_noise_sigma

							for t in timesteps:
											# 1. scale model input
											UpperCamelCase_:				Optional[int]						=    scheduler.scale_model_input(snake_case_    ,						snake_case_   )

											# 2. predict noise residual
											UpperCamelCase_:				Dict						=    model(snake_case_    ,						snake_case_   )

											# 3. predict previous sample x_t-1
											UpperCamelCase_:				str						=    scheduler.step(snake_case_    ,						snake_case_    ,						snake_case_    ,						generator=snake_case_   ).prev_sample

											UpperCamelCase_:				List[str]						=    pred_prev_sample

							UpperCamelCase_:				Union[str, Any]						=    torch.sum(torch.abs(snake_case_   )   )
							UpperCamelCase_:				str						=    torch.mean(torch.abs(snake_case_   )   )

							assert abs(result_sum.item() - 347.6357   ) < 1e-2
							assert abs(result_mean.item() - 0.4527   ) < 1e-3

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				int						=    self.scheduler_classes[0]
							UpperCamelCase_:				Optional[int]						=    self.get_scheduler_config()
							UpperCamelCase_:				Tuple						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Any						=    [39, 30, 12, 15, 0]

							with self.assertRaises(snake_case_    ,						msg="""`timesteps` must be in descending order."""   ):
											scheduler.set_timesteps(timesteps=snake_case_   )

			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				Union[str, Any]						=    self.scheduler_classes[0]
							UpperCamelCase_:				Tuple						=    self.get_scheduler_config()
							UpperCamelCase_:				List[str]						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				Tuple						=    [39, 30, 12, 1, 0]
							UpperCamelCase_:				List[Any]						=    len(snake_case_   )

							with self.assertRaises(snake_case_    ,						msg="""Can only pass one of `num_inference_steps` or `timesteps`."""   ):
											scheduler.set_timesteps(num_inference_steps=snake_case_    ,						timesteps=snake_case_   )




			def    lowerCAmelCase__						(							self			:							int   ):
							UpperCamelCase_:				Any						=    self.scheduler_classes[0]
							UpperCamelCase_:				int						=    self.get_scheduler_config()
							UpperCamelCase_:				int						=    scheduler_class(**snake_case_   )

							UpperCamelCase_:				int						=    [scheduler.config.num_train_timesteps]

							with self.assertRaises(
							    snake_case_    ,						msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""    ,						):
											scheduler.set_timesteps(timesteps=snake_case_   )






